About Our Client:

Our client is building the ad layer for the AI entertainment era. Interactive brand experiences are embedded natively across the next generation of consumer apps, games, and interactive platforms.

A bit of context on how the team thinks about itself: the market right now is chasing LLMs and AI agents. Our client is not that. This is interactive entertainment infrastructure built on traditional ML with a twist. Recommendation systems used to be one of the hottest seats in tech — think Google Ads and Instagram Ads in the mid-2010s — and they are now somewhat out of fashion as the market chases AI agents. The team is looking for engineers who want depth on real ML work rather than the AI agent hype cycle.

Backed by a16z plus a roster of gaming and ad tech strategic investors, our client closed millions in revenue commitments alongside its recent seed round. The company is currently stealth. The team is three and a half engineers today, and this is the first dedicated ML specialist hire.

About the Role:

Our client is hiring an ML Engineer to own the recommendation engine that decides, in real time, which ad reaches which user at which moment across millions of daily interactions and tens of millions in annualized ad spend.

This is a full-stack ML role. You will work across data pipelines, model architecture, and production serving, with direct business impact at every layer.

What You’ll Build:

Recommendation Engine

Design and ship a low-latency ad ranking system — retrieval, ranking, and reranking — that selects the optimal campaign and creative for each ad opportunity, balancing advertiser ROAS against user experience.

ML Training Infrastructure

Architect the data pipelines and feature stores that power continuous model training across reward signals.

User and Context Modeling

Build representations of user behavior from conversational data, engagement history, and contextual signals including geo, device, session context, and characters interacted with.

Serving Infrastructure

Build the stack for sub-second latency and cost efficiency, given tight per-impression unit economics.

Requirements

Must Have

• 0–6 years of ML engineering experience; cracked new grads welcome
• Shipped at least one ML system in production — not just research or notebooks
• Backend depth across data architecture, feature pipelines, and serving infrastructure end to end
• Hybrid infrastructure and ML background
• Zero-defect mindset with meticulous attention to latency, scalability, and reliability
• Comfort with ambiguity, including open problems such as delayed rewards, fatigue modeling, and cold start
• Bias toward shipping in an early-stage environment
• Not looking for a 9-to-5 mindset
• Based in San Francisco or willing to relocate quickly
• In-person preferred

Nice to Have

• Recommendation systems, ranking, or ad experience at scale
• PyTorch fluency
• AdTech experience, preferred but not required
• Curiosity about AI-native products and interactive entertainment

Compensation:

 $160,000 - $220,000

Equity: 0.5% - 1.0%

Job Details:

Location: San Francisco, CA

Type: In person preferred, hybrid option

Employment: Full-Time

Years of Experience: 0-6 years

Visa Sponsorship: None available